One of the tasks of a forester or forest manager is being able to accurately estimate timber volumes in a forest. In the past, volume estimates were made by sending a survey crew into a forest to obtain a sampling of tree measurements that include tree heights, diameters, spacings, etc. Estimates of timber volumes are then made by extrapolating the collected sample data to the size of the forest. While volume estimates based on sampling are generally accurate if the forest is relatively uniform, it is becoming increasingly expensive and/or logistically prohibitive to send survey crews into a sufficient number of sample areas within a large forest to obtain accurate data.
To address this problem, remote sensing is being used as an alternative technique to obtain sample data from the trees in a forest. One sensing method involves using light detection and ranging (LiDAR). With LiDAR, a low-flying aircraft, such as an airplane or helicopter, carries a LiDAR detection unit over a series of parallel paths that cover the forest area to be surveyed. The LiDAR detection unit transmits and receives laser pulses in a repeating back and forth sweep pattern for each path. The transmitted laser pulses are reflected off objects on the ground or in the air including: leaves and needles and branches, rocks, man made objects (houses, cars, telephone wires etc.), birds etc. The reflected laser pulses are detected by the LiDAR detection unit that records the time, direction, and strength of each reflected laser pulse. Because the altitude and speed of the aircraft are known as the reflected laser pulses are being detected, three dimensional coordinates for each reflected laser pulse can be determined.
While LiDAR sensing produces large amounts of data from the trees in the forest, it has been difficult to separate which laser pulses are reflected from different trees when the trees are closely spaced. The traditional approach is to analyze the LiDAR coordinate data for an object that might be a single tree. Irregularities in the data that are smaller than the expected tree size are smoothed out to make the analysis easier. The result is that the topological features that are smaller than the expected tree size are purposely ignored. However because tree sizes can vary significantly, it is difficult to know when a feature in the data is small enough to safely ignore. Therefore, laser pulses that are erroneously considered as has having been reflected from the same tree can result in an underestimate of the number of trees in a forest. Conversely, laser pulses that are erroneously considered has having been reflected from the different trees can result in an over estimate of the number of trees in a forest.
Given this problem, there is need for an improved technique of searching for individual trees in LiDAR data.